"Innovation economist tracing how cheap prediction reshapes competition."
Joshua Gans is a professor of strategic management at the University of Toronto's Rotman School of Management and one of the most prolific voices in the economics of innovation. Alongside longtime co-authors Ajay Agrawal and Avi Goldfarb, he has written widely on platforms, competition, and technological disruption, and helped build the "AI as cheap prediction" framework in Prediction Machines. In Power and Prediction, he turns that same economic lens on why so many organizations fail to capture AI's value once prediction gets cheap — and what it takes to redesign a decision system instead of just a single step of it.
Our in-depth summaries and reviews of his work
Power and Prediction summary & review: Agrawal, Gans & Goldfarb's sequel to Prediction Machines argues the real AI payoff comes from redesigning…
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Prediction Machines by Agrawal, Gans & Goldfarb explains AI as a drop in the cost of prediction — and why human judgment…
Read Summary →Gans applies rigorous economic reasoning to AI, treating prediction as a specific, tradeable good with its own supply and demand.
In Power and Prediction, he argues most AI investment plateaus because organizations redesign a step instead of the whole decision loop.
Beyond AI, Gans is known for his research on platform economics, competition, and how technological disruption reshapes industries.
"Cheaper prediction only creates value once the decisions around it are rebuilt to use it."— Power and Prediction
Power and Prediction summary & review: Agrawal, Gans & Goldfarb's sequel to Prediction Machines argues the real AI payoff comes from redesigning decision systems, not just cheaper predictions.
Prediction Machines by Agrawal, Gans & Goldfarb explains AI as a drop in the cost of prediction — and why human judgment becomes more valuable, not less. Summary, key concepts, and a 7-day plan.
